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Prompt-Tuning Can Be Much Better Than Fine-Tuning on Cross-lingual Understanding With Multilingual Language Models., , и . EMNLP (Findings), стр. 5478-5485. Association for Computational Linguistics, (2022)ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation., , , и . ACL, стр. 2819-2826. Association for Computational Linguistics, (2020)Commonsense Knowledge Base Completion., , , и . ACL (1), The Association for Computer Linguistics, (2016)Benchmarking Approximate Inference Methods for Neural Structured Prediction., и . NAACL-HLT (1), стр. 3313-3324. Association for Computational Linguistics, (2019)CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis., , , , , , , и . ICLR, OpenReview.net, (2023)An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks., , и . EMNLP (1), стр. 5569-5582. Association for Computational Linguistics, (2020)Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning., , , , , , и . EACL (Findings), стр. 1278-1294. Association for Computational Linguistics, (2024)Quality Signals in Generated Stories., , , и . *SEM@NAACL-HLT, стр. 192-202. Association for Computational Linguistics, (2018)Pay Attention to the Ending: Strong Neural Baselines for the ROC Story Cloze Task., , и . ACL (2), стр. 616-622. Association for Computational Linguistics, (2017)Learning Approximate Inference Networks for Structured Prediction., и . ICLR (Poster), OpenReview.net, (2018)